Quantitative microscopy of the Drosophila ovary shows multiple niche signals specify progenitor cell fate

Dai, Wei; Peterson, Amy; Kenney, Thomas; Burrous, Haley; Montell, Denise J.

doi:10.1038/s41467-017-01322-9

Cited by 39 publications

(73 citation statements)

References 57 publications

(77 reference statements)

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“…10 The Notch signaling pathway plays an important role in the maintenance of CSCs 565,566 and can induce CSC differentiation. Abnormal activity of the Notch signaling pathway has been observed in many cancers, such as leukemia, 567 glioblastoma, 568,569 breast cancer, 570 lung cancer, 571 ovarian cancer, 572 pancreatic cancer, 573 and colon cancer. 574 At present, there are three major clinical methods used to inhibit Notch signaling, secretase inhibition (γ-secretase inhibitor (GSI)), Notch receptor or ligand antibodies, and combination therapy with other pathways.…”

Section: Agents Targeting Csc-associated Signaling Pathways In Clinicmentioning

confidence: 99%

Targeting cancer stem cell pathways for cancer therapy

Yang

Shi

Zhao

et al. 2020

Sig Transduct Target Ther

1,090

852

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Since cancer stem cells (CSCs) were first identified in leukemia in 1994, they have been considered promising therapeutic targets for cancer therapy. These cells have self-renewal capacity and differentiation potential and contribute to multiple tumor malignancies, such as recurrence, metastasis, heterogeneity, multidrug resistance, and radiation resistance. The biological activities of CSCs are regulated by several pluripotent transcription factors, such as OCT4, Sox2, Nanog, KLF4, and MYC. In addition, many intracellular signaling pathways, such as Wnt, NF-κB (nuclear factor-κB), Notch, Hedgehog, JAK-STAT (Janus kinase/signal transducers and activators of transcription), PI3K/AKT/mTOR (phosphoinositide 3-kinase/AKT/mammalian target of rapamycin), TGF (transforming growth factor)/SMAD, and PPAR (peroxisome proliferator-activated receptor), as well as extracellular factors, such as vascular niches, hypoxia, tumor-associated macrophages, cancer-associated fibroblasts, cancer-associated mesenchymal stem cells, extracellular matrix, and exosomes, have been shown to be very important regulators of CSCs. Molecules, vaccines, antibodies, and CAR-T (chimeric antigen receptor T cell) cells have been developed to specifically target CSCs, and some of these factors are already undergoing clinical trials. This review summarizes the characterization and identification of CSCs, depicts major factors and pathways that regulate CSC development, and discusses potential targeted therapy for CSCs.Signal Transduction and Targeted Therapy (2020) 5:8; https://doi.

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Section: Agents Targeting Csc-associated Signaling Pathways In Clinicmentioning

confidence: 99%

Targeting cancer stem cell pathways for cancer therapy

Yang

Shi

Zhao

et al. 2020

Sig Transduct Target Ther

1,090

852

View full text Add to dashboard Cite

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“…Although Monocle is most commonly used to study changes in pseudotime during cellular differentiation, we reasoned that it would also be useful for identifying changes in "pseudodistance" across the IGS cell population, which exhibits an anterior-to-posterior gradient of expression of genes such as hedgehog (hh) and ptc 30,[36][37][38] ( Fig. 3N, S4A).…”

Section: Regions 1 and 2a Contain Distinct But Closely-related Populamentioning

confidence: 99%

“…This is consistent with several previous reports showing that polar cell differentiation is the earliest cell fate decision made by pFCs. 38,46,47 Polar cells induce neighboring pFCs to differentiate into stalk cells and, indeed, the next stage of differentiation in pseudotime was characterized by the upregulation of the stalk cell marker, CG46339. We also noticed that Pdk1, which we describe above as a marker of anterior and central IGS cells, is upregulated in stalk cells (Fig.…”

Section: Monocle Identifies Novel Markers Of Pfcsmentioning

confidence: 99%

A Single-Cell Atlas and Lineage Analysis of the Adult Drosophila Ovary

Rust

Byrnes

Yu³

et al. 2019

Preprint

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The Drosophila ovary is a widely used model for germ cell and somatic tissue biology. We have used single-cell RNA-sequencing to build a comprehensive cell atlas of the adult Drosophila ovary containing unique transcriptional profiles for every major cell type in the ovary, including the germline and follicle stem cells. Using this atlas we identify novel tools for identification and manipulation of known and novel cell types and perform lineage tracing to test cellular relationships of previously unknown cell types. By this we discovered a new form of cellular plasticity in which inner germarial sheath cells convert to follicle stem cells in response to starvation. Graphical AbstractManuscript (Page 3 of 31)

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“…We call this strategy Quantitative Mosaic Analysis 45 (QMA) because it replaces subjective visual comparison with a rigorous statistical 46 alternative. Although a few recent studies have deployed this approach [37][38][39][40], 47 qualitative visual comparison remains pervasive in the literature. (G) Individual nuclei are labeled homozygous mutant, heterozygous, or homozygous wildtype for the clonal marker.…”

mentioning

confidence: 99%

“…While useful in many 82 other settings, neither of these tools support automated labeling of individual cells or 83 explicit comparison of clones with single-cell resolution. Most modern studies employing 84 019 6/33 a quantitative mosaic analysis instead report using some form of ad hoc semi-automated 85 pipeline built upon ImageJ [37,39,40]. We are therefore unaware of any platforms that 86 offer comprehensive support for an automated QMA workflow.…”

mentioning

confidence: 99%

Fly-QMA: Automated analysis of mosaic imaginal discs inDrosophila

Bernasek¹,

Peláez²,

Carthew³

et al. 2019

Preprint

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Mosaic analysis provides a means to probe developmental processes in situ by generating loss-of-function mutants within otherwise wildtype tissues. Combining these techniques with quantitative microscopy enables researchers to rigorously compare RNA or protein expression across the resultant clones. However, visual inspection of mosaic tissues remains common in the literature because quantification demands considerable labor and computational expertise. Practitioners must segment cell membranes or cell nuclei from a tissue and annotate the clones before their data are suitable for analysis.Here, we introduce Fly-QMA, a computational framework that automates each of these tasks for confocal microscopy images of Drosophila imaginal discs. The framework includes an unsupervised annotation algorithm that incorporates spatial context to inform the genetic identity of each cell. We use a combination of real and synthetic validation data to survey the performance of the annotation algorithm across a broad range of conditions. By contributing our framework to the open-source software ecosystem, we aim to contribute to the current move toward automated quantitative analysis among developmental biologists. Author summaryBiologists use mosaic tissues to compare the behavior of genetically distinct cells within an otherwise equivalent context. The ensuing analysis is often limited to qualitative insight. However, it is becoming clear that quantitative models are needed to unravel the complexities of many biological systems. In this manuscript we introduce Fly-QMA, an open-source software framework that automates the quantification of mosaic analysis for Drosophila imaginal discs, a common setting for studies of developmental processes.The software automatically extracts quantitative measurements from confocal images of mosaic tissues, rectifies any cross-talk between fluorescent reporters, and identifies clonally-related subpopulations of cells. Together, these functions allow users to rigorously ascribe changes in gene expression to the presence or absence of particular genes. We validate the performance of our framework using both real and synthetic data. Through its publication, we aim to contribute to the current move toward Introduction 1 Quantification will be essential as biologists study increasingly complex facets of 2 organismal development [1]. Unfortunately, qualitative analysis remains common 3 because it is often difficult to measure cellular processes in their native context. Modern 4 fluorescent probes and microscopy techniques make such measurements possible [2-4], 5but the ensuing image analysis demands specialized skills that fall beyond the expertise 6 of most experimentalists. Automated analysis strategies have addressed similar 7 challenges in cytometry [5][6][7], genomics and transcriptomics [8][9][10][11], and other 8 subdisciplines of biology [12,13]. Image analysis has proven particularly amenable to 9 automation, with several computer vision tools having gained traction among 10 biologists [14-17]....

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Quantitative microscopy of the Drosophila ovary shows multiple niche signals specify progenitor cell fate

Cited by 39 publications

References 57 publications

Targeting cancer stem cell pathways for cancer therapy

Targeting cancer stem cell pathways for cancer therapy

A Single-Cell Atlas and Lineage Analysis of the Adult Drosophila Ovary

Fly-QMA: Automated analysis of mosaic imaginal discs inDrosophila

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